...work with speech and audio

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Use this page when your data is sound: calls, meetings, voice commands, podcasts, machine noises, animal calls. Speech AI has three building blocks (speech-to-text, a language model, text-to-speech), and newer models can go straight from speech to speech.

Start here

  • Turn speech into text (captions, call analytics, notes) → automatic speech recognition (ASR): Whisper and other open models, or cloud speech-to-text (Google, Microsoft, AWS, Deepgram, AssemblyAI, ElevenLabs) → Speech Recognition
    • Need "who said what" → add speaker diarization (e.g., pyannote)
    • Lots of jargon or names → custom vocabulary, or fine-tune on your recordings
    • Compare accuracy and speed on the Open ASR Leaderboard, then on your own audio
  • Turn text into speech (voice-overs, accessibility, phone menus) → neural text-to-speech (ElevenLabs, OpenAI, Microsoft, Google; open models such as Kokoro and Piper). Cloning a real voice needs that person's consent and clear disclosure.
  • Hold a real-time spoken conversation (voice agent, phone assistant)
    • Cascaded pipeline: speech-to-text → LLM → text-to-speech. Most control, any LLM, easy to log and test; adds some latency
    • Speech-to-speech models (e.g., OpenAI Realtime, Gemini Live): lower latency and natural turn-taking; less control over each step
    • Frameworks: LiveKit Agents, Pipecat; connect to phone lines through a telephony provider. Tools and actions → ...build an agent
  • Translate speech → a cascade (speech-to-text → translation → text-to-speech) or direct speech translation models (e.g., Meta SeamlessM4T)
  • Recognize sounds (alarms, machine faults, animals) → audio classifiers (YAMNet, Audio Spectrogram Transformer); birds → BirdNET; unusual machine sounds → ...find outliers
  • Clean or separate audio → noise suppression, source separation (e.g., Demucs)
  • Create music or sound effects → ...generate content

Quick picks

Need Try first Then compare
Batch transcription Whisper (open) or a cloud ASR Domain-tuned ASR
Live captions Streaming cloud ASR Small on-device ASR
Voice-over Neural TTS Custom voice (with consent)
Voice agent, needs control Cascaded STT → LLM → TTS Speech-to-speech model
Voice agent, lowest latency Speech-to-speech model Cascade with streaming at every step
Sound events Pretrained audio classifier Fine-tuned classifier on your recordings

Real-world examples

  • Advisor meeting notes — Morgan Stanley's AI @ Morgan Stanley Debrief records client meetings with consent, then produces notes, action items and a draft follow-up email for the advisor to review. AI @ Morgan Stanley Debrief launch | Morgan Stanley
  • Wildlife monitoring — the Cornell Lab of Ornithology's BirdNET identifies bird species from their sounds, helping researchers and volunteers monitor biodiversity at scale. BirdNET | Cornell Lab of Ornithology

Watch out

  • Consent and disclosure — tell people when they are recorded or talking to an AI; follow local recording laws.
  • Accents, noise and crosstalk raise error rates; test with your real callers and rooms.
  • Voice fraud — cloned voices can defeat voice authentication; do not rely on voice alone to verify identity.

Measure success

Word error rate (WER), diarization error rate, latency (time to first audio for voice agents), naturalness ratings (MOS), and task success for voice agents. More: ...evaluate and trust AI

Learn more

Where next? ◀ Understand unstructured data · ...understand language · ...build an agent · ...generate content · ▶ ...evaluate and trust AI
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